AWS Aurora vs Snowflake: 5 Critical Differences

Nowadays, with ever-increasing data, storage and analysis of large volumes of data have become basic foundations of any organization. While focusing on storage and analysis, which two concepts come to your mind? That’s a database and a data warehouse, right? And database management systems (DBMS) act as the interface between the user and the database.

As a data engineer, you would have also wondered about the choice of a DBMS and a data warehouse based on the kind of data that needs to be stored. And one such scenario is comparing AWS Aurora and Snowflake. If so, then don’t worry, you’re in the right place.

In this guide, we have prepared a list of the parameters for making the AWS Aurora vs Snowflake comparison. Let’s dive in!

Key Differences between AWS Aurora and Snowflake

AWS Aurora is a global-scale cloud-based relational database management system (RDBMS) that is fully compatible with MySQL and PostgreSQL. Aurora provides the performance and availability of commercial-grade databases at one-tenth of the price.

Snowflake is a fully managed SaaS platform that offers pre-built capabilities, including storage and compute separation, data sharing, data cloning, on-the-fly scalable compute, and third-party integrations support to extend the desired capabilities.

We have compiled a list of parameters based on which the AWS Aurora vs Snowflake comparison can be made. 

We’ll start with the working model of the two platforms.

AWS Aurora vs Snowflake: Working Model

An AWS Aurora DB cluster is made up of one or more database instances and a cluster volume that handles the data for those database instances. An Aurora cluster volume is a virtual database storage volume that spans several availability zones, with a copy of the DB cluster data in each availability zone. An Aurora DB cluster consists of two types of DB instances:

• Primary DB instance: It supports read and write operations and handles all data changes to the cluster volume. There is only one primary DB instance in each Aurora DB cluster.
• Aurora Replica: It connects to the same storage volume as the primary database instance and only allows read operations. In addition to the core DB instance, each Aurora DB cluster can have up to 15 Aurora replicas.  

The diagram below depicts the relationship between an Aurora DB cluster’s cluster volume, primary DB instance, and Aurora replicas.

The primary objective of data warehousing is to have a centralized repository where data is stored from various sources in analysis-ready form. It stores large amounts of historical data which are solely intended to be queried or analyzed. Snowflake’s working model includes three tiers: 

• Database Storage: The data is loaded and stored in the bottommost layer, which is known as Database storage. Snowflake reorganizes that data into its internal optimized, compressed, and columnar format before storing the data.
• Query Processing: As the middle layer of the Snowflake working model, it provides a handy platform for analysis. In this layer, Snowflake processes queries using “virtual warehouses.”
• Front-end client layer: The results after performing analysis, reporting, and data mining are visible to the user on the topmost front-end client layer. This layer includes a collection of services that combines and coordinates all the activities across Snowflake. 

AWS Aurora vs Snowflake: User Persona

AWS Aurora is most often used by companies with more than 10000 employees and annual revenue greater than 1000M dollars. It is used by companies as a cloud database management system. Since it is compatible with MySQL and PostgreSQL, data engineers and people with some technical background can work with it.

Snowflake is administered by a data engineer or database administrator (IT) but is significantly important to everyone in the organization since it is the centralized source of data. Users (typically analysts) can build their reports by transforming data according to their use cases. The Snowflake admin should be well versed with SQL and programming ability in languages such as Python, Java, Scala, etc., while the business users can easily work on the warehouse with basic SQL knowledge.

AWS Aurora vs Snowflake: Use Cases

AWS Aurora has a variety of use cases. Some of them are listed below:

• Software as a Service (SaaS): It usually includes multi-talented architectures that should be flexible in terms of instance and storage scalability. It even enables businesses to focus on developing high-quality apps rather than worrying about the underlying database.
• Web & mobile gaming purpose: As AWS Aurora has no licensing restrictions, it perfectly matches the variable usage pattern (depending on different user behavior profiles) of the gaming applications. Furthermore, web and mobile games designed to run on a big scale require a database with fast throughput, vast storage scalability, and high availability, which AWS Aurora provides.
• Enterprise Application: AWS Aurora is ideally suited for any organization that is already familiar with relational databases. Aurora is cost-effective because it reduces database costs by 90% when compared to its competitors. It also enhances database reliability and availability. As a fully managed service, AWS Aurora saves customers time by automating time-consuming processes such as provisioning, patching, backup, failure detection, recovery, and repair.

Being a cloud data warehousing solution, Snowflake has a variety of use cases. These include:

• Storage: Snowflake, being a cloud data warehouse, is a great alternative to an on-prem data storage solution. It is a highly scalable, fully-managed durable, and cost-effective storage solution.
• Reporting: Snowflake will act as a single source of truth for all your data. Hence, any data you need for preparing reports will be available in this centralized repository. Thus, it will facilitate the reporting process. In addition to that, numerous BI & reporting tools can be integrated effortlessly with Snowflake.
• Analytics: Snowflake allows you to execute data analysis at any scale to gain insights. Its wide integration with various tools will help add value to operational business applications. 

AWS Aurora vs Snowflake: Limitations

Some of the limitations associated with AWS Aurora are as follows:

• Need for an increase in storage space: Even though the maximum storage capacity has increased from 64TiB to 128TiB, but for some large organizations, even this storage capacity won’t be enough.
• Complicated Interface: The UI is a bit old and can be complicated for beginners who are just beginning to use the platform. 
• Limited resources for small instances: Owing to its limited amount of resources and connections, AWS Aurora is not considered the ideal solution for testing or developing small and medium instances.

Snowflake is a widely accepted data warehouse solution with numerous advantages. Its performance and ease of use help it stand out from others. Despite this, there are also a few limitations associated with Snowflake. These limitations are:

• Lack of support for unstructured data: Snowflake only supports storing structured and semi-structured data.
• No data constraints: Owing to its flexible scalability, sometimes Snowflake can prove to be over-pricey. As Snowflake has a pay-as-you-go model, users might incur expensive bills if they don’t keep track of their storage capacity.
• Only bulk data load: While moving data to Snowflake, there is extensive support and guidance on bulk data loading. However, if users require continuous loading, they are limited to using Snowpipe, which isn’t robust and doesn’t have extensive support.

AWS Aurora vs Snowflake: Alternatives

When evaluating different solutions, potential buyers seek different features in a database management system that best fits their use cases. The features fall into categories such as evaluation and contracting, service and support, integration and deployment, and specific product capabilities. Other important factors to consider when looking for alternatives to AWS Aurora include storage and performance.

Here’s a list of some of the tools which can be considered as an alternative to AWS Aurora:

• Oracle Database: Better at service & support, easier to integrate & deploy, better evaluation & contracting.
• Azure SQL Database: Easier to set up, more usable, and easy to administer.
• Google Cloud Spanner: Better for massive scale opportunities.
• Microsoft SQL Server: Easy to set up and configure, comprehensive documentation and easier to maintain.
• MariaDB: More usable, easier to set up, open source, easier learning curve and great performance free of cost.
• MongoDB Atlas: Free trial & freemium version available, support virtualization, easier to set up and do business with.

People seek different features in a data warehouse solution that best fits their use cases. The features can be easy to use, reliable software solutions with built-in data analytics, data lake integration, and AI/ML integration. Other important factors considered when looking for alternatives to Snowflake include features, pricing, user interface, and the number of integrations.

Here’s the list of the top 6 alternatives of Snowflake:

• AWS Redshift: Better at support & service, easier to integrate and deploy.
• Databricks Lakehouse Platform: More robust ELT and machine learning features.
• IBM Db2: Ideal for businesses considering hybrid architecture.
• Azure Synapse Analytics: Better at support and easier to administer and serves immediate BI & ML needs.
• Azure Data Lake Storage: Secures data lake for customers’ high-performance analytics workloads.

Refer to our blog on top open source alternatives of Snowflake to learn more about the alternatives tools of Snowflake.

Summing It Up

In this article, you got an in-depth analysis of the parameters based on which you can make the AWS Aurora vs Snowflake comparison.

After going through the article, all your doubts would have been cleared regarding choosing between AWS Aurora and Snowflake. See how Aurora MariaDB can provide robust solutions for your database needs with high availability and performance.

There would be times when you would feel that getting data into Snowflake or MySQL Amazon Aurora can be a time-consuming and resource-intensive task, especially if you have multiple data sources. Instead of spending months developing and maintaining such data integrations, you can enjoy a smooth ride with Hevo Data’s 150+ plug-and-play integrations (including 60+ free sources).

Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag-n-drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo Data’s interface and get your data in the final analysis-ready form. 

FAQ on AWS Aurora vs Snowflake